In this paper, a novel neural network-based error-track iterative learning control scheme is proposed to tackle trajectory tracking problem for tank gun control systems. Firstly, the system modeling for tank gun control systems is introduced as a preparation of controller design. Then, the reference error trajectory is constructed to deal with the nonzero initial error of iterative learning control. The adaptive iterative learning controller for tank gun control systems is designed by using Lyapunov approach. Adaptive learning neural network is adopted to approximate nonlinear uncertainties, with robust control technique being used compensate the approximation error and external disturbances. As the iteration number increases, the system error can follow the desired error trajectory over the whole time interval, which makes the system state accurately track the reference error trajectory during the predetermined part time interval. Numerical simulations demonstrate the effectiveness of the proposed iterative learning control scheme. INDEX TERMS Tank gun control systems, iterative learning control, neural network, Lyapunov approach.
In this paper, the position tracking control for tank gun control systems with periodic reference signal is studied. On the basis of corresponding system modeling, a novel repetitive controller is developed by using Lyapunov synthesis. During the controller design, signal replacement mechanism is used to deal with the nonparametric uncertainties under Lipschitz-like continuous condition, and repetitive learning laws are developed to estimate the unknown periodic parameters. Meanwhile, robust learning approach is used to compensate the sum of random disturbances, whose upper bound is estimated according repetitive learning mechanism. Hyperbolic tangent function, rather than sign function, is applied to design a robust feedback term to release the occurrence of chattering phenomenon. Numerical simulations demonstrate the effectiveness of the proposed repetitive control scheme.
INDEX TERMSTank gun control systems, repetitive control, Lyapunov approach, Lipschitz-like continuous condition. JINGHUA TIAN received the B.S. degree in computer science and technology from the Huazhong University of Science and Technology, in 2002, and the M.S. degree in communication and information system from the Zhejiang University of Technology. She was with the Zhejiang University of Water Resources and Electric Power, Hangzhou, in 2004, where she was an Assistant with the Information Engineering and Art Design Department. Since 2004, she has been working as a Lecturer with the Zhejiang University of Water Resources and Electric Power. Her current research interests include automatic control, industrial automation, computer networking technology, and communication technology.
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